simple inner product calculation which is 
suitable for parallel processing. 
4. SUMMARY 
A new approach to the unmixing problem by 
the subspace method is proposed and applied to 
wetland vegetation using hyperspectral imagery. 
Unmixing by the subspace method is superior to 
conventional methods in numerical stability and 
computation speed for hyper spectral imagery. 
The results of the unmixing experiment showed 
unmixing by subspace is spatially accurate 
except for the classes that are spectrally very 
similar. In the near future, the number of 
sensor channels and the size of image area will 
rapidly increase. The fast and stable unmixing 
algorithm based on the subspace method will be 
most useful for such data. Further, we need to 
improve the separability between the spectrally 
very similar classes by developing the present 
approach. 
5. ACKNOWLEDGMENT 
For the acquisition of airborne data and survey 
of vegetation investigation, I would like to thank 
Mr. Oguma of NASDA. This analysis was 
conducted in the project entitled “Global 
wetland mapping using remotely sensed data” 
by the Environmental Agency of Japan. 
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